Camera enabled portal

ABSTRACT

A delivery portal, which may be at a loading dock, includes a sensor configured to detect a pallet, platform or stack of goods as it passes through the portal. A computer is programmed to receive information from the sensor and to identify the pallet based upon the information. The computer is further programmed to compare the identified pallet to a database to determine if the identified pallet should be passing through the portal. For example, the computer determines whether the pallet is being loaded onto the wrong truck or onto the right truck but in the wrong sequence. The sensor for detecting the pallet may be an RFID sensor reading an RFID tag on the pallets. The portal may be a loading dock. The database may indicate a sequence for loading a plurality of pallets including the identified pallet onto a truck at the loading dock.

BACKGROUND

A truck leaving a distribution center may contain numerous pallets eachloaded with goods. One or more of the loaded pallets may be required tobe delivered to each of a plurality of stores. Attempts are made to loadthe truck in reverse-sequence, that is, loading the last-to-be-deliveredfirst. Loading the pallets in the wrong sequence can reduce efficiency.Loading pallets into the wrong truck can significantly reduceefficiency.

SUMMARY

A delivery portal, which may be at a loading dock, includes a sensorconfigured to detect a pallet, platform or stack of goods as it passesthrough the portal. A computer is programmed to receive information fromthe sensor and to identify the pallet based upon the information. Thecomputer is further programmed to compare the identified pallet to adatabase to determine if the identified pallet should be passing throughthe portal. For example, the computer determines whether the pallet isbeing loaded onto the wrong truck or onto the right truck but in thewrong sequence.

The sensor for detecting the pallet may be an RFID sensor reading anRFID tag on the pallets. The portal may be a loading dock.

The database may indicate a sequence for loading a plurality of palletsincluding the identified pallet onto a truck at the loading dock.

The delivery portal may also include a camera and the computer may beprogrammed to receive images from the camera. The computer may also beprogrammed to identify a person moving the pallet through the portal,such as via facial recognition based on the image from the camera.

The computer may be programmed to determine a direction of travel of thepallet through the portal. The computer may determine the direction oftravel based upon information from the camera, such as based upon aplurality of sequential images from the camera. In this manner, thecomputer can track whether the identified pallet is being moved onto thetruck or off of the truck (for example, after it has been noted that awrong pallet has been moved onto the truck).

The delivery portal may further include a presence sensor. The computermay be programmed to activate the RFID sensor and/or the camera basedupon information from the presence sensor. The presence sensor may be abreakbeam sensor or a motion sensor.

Also disclosed herein is a delivery portal sensor tower, which can beused, for example, at a loading dock. The tower may include a housingand an RFID sensor, a camera, and a presence sensor all mounted to thehousing. A computer may be in communication with the RFID sensor, thecamera and the presence sensor. Based upon an indication of presence bythe presence sensor, the computer is programmed to cause the RFID sensorto read an RFID tag and to cause the camera to generate at least oneimage.

A computerized method for operating a portal is also disclosed herein. Aplatform carrying a plurality of items stacked thereon is identifiednear a truck. The identity of the platform is received in computer. Thecomputer compares the identified platform to a list indicating whetherthe identified platform should be loaded onto the truck. The computergenerates an indication whether the identified platform should be loadedonto the truck.

The platform may be a pallet. The list may indicate a sequence ofloading a plurality of pallets including the identified pallet. Thecomputer compares the identified pallet to the list to determine whetherothers of the plurality of pallets on the list should be loaded onto thetruck before the identified pallet.

The platform or pallet may be identified by reading an RFID tag on thepallet or platform. The camera may be used to image the platform orpallet and a person moving the platform or pallet. The image may be usedto validate the items on the pallet or platform, and may be used toidentify the person.

The method may also include determining a direction of movement of theplatform relative to the truck, e.g. whether the platform or pallet isbeing moved onto the truck or off of the truck.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a delivery system.

FIG. 2 is a flowchart of one version of a method for assembling itemsfor delivery.

FIG. 3 shows an example loading station of the delivery system of FIG.1.

FIG. 4 shows an example validation station of the delivery system ofFIG. 1.

FIG. 5 is another view of the example validation system of FIG. 4 with aloaded pallet thereon.

FIG. 6 shows an example loading station of the delivery system of FIG.1.

FIG. 7 is another view of the example loading station of FIG. 6.

FIG. 8 illustrates a sensor tower at the loading dock of FIG. 7.

FIG. 9 shows a portion of the sensor tower of FIG. 8 partially brokenaway.

FIG. 10 shows a sensor tower positioned adjacent each doorway and aloaded pallet being brought toward the doorway.

FIGS. 11A and 11B show a flowchart for the operation of the sensortower.

FIG. 12 shows break beam sensors detecting an outbound loaded pallet.

FIG. 13 shows the RFID reader detecting a tag.

FIG. 14 shows the camera capturing an image.

FIG. 15 illustrates the camera capturing images for the SKU validationand the load validation.

FIG. 16 shows the breakbeam sensor detecting movement of an inboundloaded pallet.

FIG. 17 shows the RFID sensor recording the RFID tag on the pallet.

FIG. 18 shows the camera imaging the loaded pallet and the driver.

FIG. 19 shows that the system has determined the direction, date/time,pallet id and identification of the driver.

DETAILED DESCRIPTION

FIG. 1 is a high-level view of a delivery system 10 including one ormore distribution centers 12, a central server 14 (e.g. cloud computer),and a plurality of stores 16. A plurality of trucks 18 or other deliveryvehicles each transport the products 20 on pallets 22 from one of thedistribution centers 12 to a plurality of stores 16. Each truck 18carries a plurality of pallets 22 which may be half pallets, each loadedwith a plurality of goods 20 for delivery to one of the stores 16. Awheeled sled 24 is on each truck 18 to facilitate delivery of one ofmore pallets 22 of goods 20 to each store 16. Generally, the goods 20could be loaded on the half pallets 22, full-size pallets, carts, orhand carts, or dollies—all considered “platforms” herein.

Each distribution center 12 includes one or more pick stations 30, aplurality of validation stations 32, and a plurality of loading stations34. Each loading station may be a loading dock for loading the trucks18.

Each distribution center 12 includes a DC computer 26. The DC computer26 receives orders 60 from the stores 16 and communicates with a centralserver 14. Each DC computer 26 receives orders and generates pick sheets64, each of which stores SKUs and associates them with pallet ids.Alternatively, the orders 60 can be sent from the DC computer 26 to thecentral server 14 for generation of the pick sheets 64, which are syncedback to the DC computer 26.

Some or all of the distribution centers 12 may include a trainingstation 28 for generating image information and other information aboutnew products 20 which can be transmitted to the central server 14 foranalysis and future use.

The central server 14 may include a plurality of distribution centeraccounts 40, including DC1-DCn, each associated with a distributioncenter 12. Each DC account 40 includes a plurality of store accounts 42,including store 1-store n. The orders 60 and pick sheets 64 for eachstore are stored in the associated store account 42. The central server14 further includes a plurality of machine learning models 44 trained aswill be described herein based upon SKUs. The models 44 may beperiodically synced to the DC computers 26.

The machine learning models 44 are used to identify SKUs. A “SKU” may bea single variation of a product that is available from the distributioncenter 12 and can be delivered to one of the stores 16. For example,each SKU may be associated with a particular package type, e.g. thenumber of containers (e.g. 12 pack) in a particular form (e.g. can prbottle) and of a particular size (e.g. 24 ounces) with a particularsecondary container (cardboard vs reusuable plastic crate, cardboardtray with plastic overwrap, etc). Each machine learning model 44 istrained to identify the possible package types.

Each SKU may also be associated with a particular “brand” (e.g. themanufacturer and the specific flavor). Each machine learning model 44 istrained to identify the possible brands, which are associated with thename of the product, a description of the product, dimensions of theproduct, and image information for the product. The central server 14also stores the expected weight of each SKU. It is also possible thatmore than one variation of a product may share a single SKU, such aswhere only the packaging, aesthetics, and outward appearance of theproduct varies, but the content and quantity is the same. For example,sometimes promotional packaging may be utilized, which would havedifferent image information for a particular SKU. In general, all themachine learning models 44 may be generated based upon image informationgenerated through the training module 28.

Referring also to the flowchart in FIG. 2, an order 60 may be receivedfrom a store 16 in step 150. As an example, an order 60 may be placed bya store employee using an app or mobile device 52. The order 60 is sentto the distribution center computer 26 (or alternatively to the server14, and then relayed to the proper (e.g. closest) distribution centercomputer 26). The distribution center computer 26 analyzes the order 60and creates a pick sheet 64 associated with that order 60 in step 152.The pick sheet 64 assigns each of the SKUs (including the quantity ofeach SKU) from the order. The pick sheet 64 specifies how many pallets22 will be necessary for that order (as determined by the DC computer26). The DC computer 26 may also determine which SKUs should be loadednear one another on the same pallet 22, or if more than one pallet 22will be required, which SKUs should be loaded together on the samepallet 22. For example, SKUs that go in the cooler may be together onthe same pallet (or near one another on the same pallet), while SKUsthat go on the shelf may be on another part of the pallet (or on anotherpallet, if there is more than one). If the pick sheet 64 is created onthe DC computer 26, it is copied to the server 14. If it is created onthe server 14, it is copied to the DC computer 26.

FIG. 3 shows the pick station 30 of FIG. 1. Referring to FIGS. 1 and 3,workers at the distribution center read the palled id (e.g. via rfid,barcode, etc) on the pallet(s) 22 on a pallet jack 24 a, such as with amobile device or a reader on the pallet jack 24 a. Shelves may contain avariety of items 20 for each SKU, such as first product 20 a of a firstSKU and a second product 20 b of a second SKU (collectively “products20”). A worker reading a computer screen or mobile device screendisplaying from the pick sheet 64 retrieves each product 20 and placesthat product 20 on the pallet 22. Alternatively, the pallet 22 may beloaded by automated handling equipment.

Workers place items 20 on the pallets 22 according to the pick sheets64, and report the palled ids to the DC computer 26 in step 154. The DCcomputer 26 dictates merchandizing groups and sub groups for loadingitems 20 a, b on the pallets 22 in order to make unloading easier at thestore. In the example shown, the pick sheets 64 dictate that products 20a are on one pallet 22 while products 20 b are on another pallet 22. Forexample, cooler items should be grouped, and dry items should begrouped. Splitting of package groups is also minimized to make unloadingeaser. This makes pallets 22 more stable too.

After one pallet 22 is loaded, the next pallet 22 is brought to the pickstation 30, until all of the SKUs required by the pick sheet 64 areloaded onto as many pallets 22 as required by that pick sheet 64. Morepallets 22 are then loaded for the next pick sheet 64. The DC computer26 records the pallet ids of the pallet(s) 22 that have been loaded withparticular SKUs for each pick sheet 64. The pick sheet 64 may associateeach pallet id with each SKU.

After being loaded, each loaded pallet 22 may be validated at thevalidation station 32, which may be adjacent to or part of the pickstation 30. As will be described in more detail below, at least onestill image, and preferably several still images or video, of theproducts 20 on the pallet 22 is taken at the validation station 32 instep 156. The pallet id of the pallet 22 is also read. The images areanalyzed to determine the SKUS of the products 20 that are currently onthe identified pallet 22 in step 158. The SKUs of the products 20 on thepallet 22 are compared to the pick sheet 64 by the DC computer 26 instep 160, to ensure that all the SKUs associated with the pallet id ofthe pallet 22 on the pick sheet 64 are present on the correct pallet 22,and that no additional SKUs are present. Several ways are of performingthe aforementioned steps are disclosed below.

First, referring to FIGS. 4 and 5, the validation station may include aCV/RFID semi-automated wrapper 66 a with turntable 67 may be speciallyfitted with a camera 68 and rfid reader 70 (and/or barcode reader). Thewrapper 66 a holds a roll of translucent, flexible, plastic wrap orstretch wrap 72. As is known, a loaded pallet 22 can be placed on theturntable 67, which rotates the loaded pallet 22 as stretch wrap 72 isapplied. The camera 68 may be a depth camera. In this wrapper 66 a, thecamera 68 takes at least one image of the loaded pallet 22 while theturntable 67 is rotating the loaded pallet 22, prior to or whilewrapping the stretch wrap 72 around the loaded pallet 22. Images/videoof the loaded pallet 22 after wrapping may also be generated. As usedherein, “image” or “images” refers broadly to any combination of stillimages and/or video, and “imaging” means capturing any combination ofstill images and/or video. Again, preferably 2 to 4 still images, orvideo, are taken.

In one implementation, the turntable 67 is rotating and when the camera68 detects that the two outer ends of the pallet 22 are equidistant (orotherwise that the side of the pallet 22 facing the camera 68 isperpendicular to the camera 68 view), the camera 68 records a stillimage. The camera 68 can record four still images in this manner, one ofeach side of the pallet 22.

The rfid reader 70 (or barcode reader, or the like) reads the pallet id(a unique serial number) from the pallet 22. The wrapper 66 a includes alocal computer 74 in communication with the camera 68 and rfid reader70. The computer 74 can communicate with the DC computer 26 (and/orserver 14) via a wireless network card 76. The image(s) and the palletid are sent to the server 14 via the network card 76 and associated withthe pick list 64 (FIG. 1). Optionally, a weight sensor can be added tothe turntable 67 and the known total weight of the products 20 andpallet 22 can be compared to the measured weight on the turntable 67 forconfirmation. An alert is generated if the total weight on the turntable67 does not match the expected weight.

As an alternative, the turntable 67, camera 68, rfid reader 70, andcomputer 74 of FIGS. 4 and 5 can be used without the wrapper. The loadedpallet 22 can be placed on the turntable 67 for validation only and canbe subsequently wrapped either manually or at another station.

Alternatively, the validation station can include a worker with anetworked camera, such as on a mobile device (e.g. smartphone or tablet)for taking one or more images 62 of the loaded pallet 22, prior towrapping the loaded pallet 22. Other ways can be used to gather imagesof the loaded pallet. In any of the methods, the image analysis and/orcomparison to the pick list is performed on the DC computer 26, whichhas a copy of the machine learning models. Alternatively, the analysisand comparison can be done on the server 14, locally on a computer 74,or on the mobile device 78, or on another locally networked computer.

However the image(s) of the loaded pallet 22 are collected, the image(s)are then analyzed to determine the sku of every item 20 on the pallet 22in step 158 (FIG. 2).

The computer vision-generated sku count for that specific pallet 22 iscompared against the pick list 64 to ensure the pallet 22 is builtcorrectly. This may be done prior to the loaded pallet 22 being wrappedthus preventing unwrapping of the pallet 22 to audit and correct. If thebuilt pallet 22 does not match the pick list 64 (step 162), the missingor wrong SKUs are indicated to the worker (step 164). Then the workercan correct the items 20 on the pallet 22 (step 166) and reinitiate thevalidation (i.e. initiate new images in step 156). If the loaded pallet22 is confirmed, positive feedback is given to the worker, who thencontinues wrapping the loaded pallet 22 (step 168). The worker thenmoves the validated loaded pallet 22 to the loading station 34 (step172).

After the loaded pallet 22 has been validated, it is moved to a loadingstation 34 (FIG. 1). As explained in more detail below, at the loadingstation 34, the distribution center computer 26 ensures that the loadedpallets 22, as identified by each pallet id, are loaded onto the correcttrucks 18 in the correct order. For example, pallets 22 that are to bedelivered at the end of the route are loaded first.

Referring to FIGS. 1 and 6, a computer (DC computer 26, server 14, oranother) determines efficient routes to be driven by each truck 18 tovisit each store 16 in the most efficient sequence, the specific loadedpallets 22 that must go onto each truck 18, and the order in which thepallets 22 should be loaded onto the trucks 18. An optimized queuesystem is used to queue and load loaded pallets 22 onto the truck 18 inthe correct reverse-stop sequence (last stop is loaded onto the truck 18first) based upon the route planned for that truck 18. Each truck 18will be at a different loading dock doorway 80. A list or database mayindicate which pallets 22 are to be loaded into which trucks 82 and inwhich sequence.

FIG. 7 shows an example loading station 34, such as a loading dock witha doorway 80. Based upon the sequence determined by the server 14 or DCcomputer 26 or other computer, an electronic visual display 82 proximatethe doorway 80 shows which pallet 22 is to be loaded onto that truck 18next. A sensor tower 310 having a housing 312 is mounted adjacent thedoorway 80. A presence sensor 316 may be mounted to the housing 312. Thesensor tower 310 may further include a camera 84 and/or rfid reader 86adjacent the doorway 80. After being triggered by the presence sensor316, the camera 84 and/or the rfid reader 86 image/read each loadedpallet 22 as it is being loaded onto the truck 18. The pallet 22 may beidentified by the pallet id and/or based upon the products on the palletas shown in the image. The computer compares that identified pallet 22to the previously-determined lists.

If the wrong pallet 22 is moved through (or toward) the doorway 80, anaudible and/or visual alarm alerts the workers. Optionally, the rfidreader 86 at the doorway 80 is able to determine the direction ofmovement of the rfid tag on the loaded pallet 22, i.e. it can determineif the loaded pallet 22 is being moved onto the truck 18 or off of thetruck 18. This is helpful if the wrong loaded pallet 22 is moved ontothe truck 18. The worker is notified that the wrong pallet 22 wasloaded, and the rfid reader 86 can confirm that the pallet was thenmoved back off the truck 18.

When a group of loaded pallets 22 (two or more) is going to the samestore 16, the loaded pallets 22 within this group can be loaded onto thetruck 18 in any order. The display 82 may indicate the group of loadedpallets 22 and the loaded pallets 22 within this group going to the samestore 16 will be approved by the rfid reader 86 and display 82 in anyorder within the group.

FIG. 8 shows the sensor tower 310 that could be used, for example, atthe doorway 80 at the loading dock of FIG. 7. FIG. 9 shows a portion ofthe sensor tower 310, partially broken away. The sensor tower 310includes the housing 312 supporting above the floor the RFID reader 86(which could be a UHF RFID reader), the presence sensor such as a breakbeam sensor 316, and the camera 84 (which could be a depth camera, asabove). The RFID reader 86, break beam sensor 316, and camera 84 may allbe controlled by the DC computer 26. Alternatively, a local computer(e.g. in the tower 310) is programmed to control the operation of thesedevices and to communicate with the DC computer 26.

As shown in FIG. 10, the sensor tower 310 is positioned adjacent eachdoorway 80 at each loading station 34, with the RFID reader 86, breakbeam sensor 316 (which could be photo optic), and camera 84 all directedtoward the doorway 80. The sensor tower 310 could also be mounted at anyentrance or exit or any point where tracking asset moves would bebeneficial. The display 82 is mounted near the doorway 80, such as abovethe doorway 80.

As also shown in FIG. 10, a forklift 328 (or pallet jack or pallet sledor any machine for lifting and moving pallets) operated by an operator330, is moving a pallet 22 having an RFID tag 94. The pallet 22 isloaded with products 20. As the loaded pallet 22 is moved through thedoorway 80, it passes in front of the sensor tower 310.

The computer, such as the DC computer 26, the server 14, or a dedicatedlocal computer (or some combination thereof) is programmed to performthe steps shown FIGS. 11A and 11B. Referring to FIG. 11A and FIG. 12,the loaded pallet 22 passes through the doorway 80 (or as it approachesthe doorway 80), the break beam sensor 316 detects presence in step 340,the RFID reader 86 and the camera 84 are activated in steps 342 and 344,respectively. If the RFID reader 86 detects a tag 94 (FIGS. 11A and 13),the tag 94 is read in step 346 and checked against known tags. If thetag 94 is identified in the system in step 348, it is recorded in step352. If the tag 94 is not identified, it is determined that there is noloading event in step 350. For example, maybe a person or equipmentpassed in front of the break beam sensor 316 without a pallet 22.

Simultaneously with step 342, the camera 84 will start capturing imagesin step 356 (FIGS. 11A and 14). Two images taken at some short timeinterval apart (e.g. 1 second or less) are compared in step 358. Basedupon the comparison of the two images, the direction of movement of thepallet 22, goods 20, and/or the lift 438 can be determined (such as bythe DC computer, server, or local computer. It can also be determined bythe computer whether the driver/operator 330 is in the image(s) in steps364, 366. Referring to FIG. 11B, a person shape image within the imageis identified in step 366. The person image is processed in step 368,e.g. via facial recognition. Alternatively, or additionally, the personmay also have an RFID tag that can be read by the RFID reader 86. If aperson is identified in step 370, then the known person is recorded instep 372. If not, then “person unknown” is recorded in step 374. Thesystem may ensure that the person identified is authorized to be in thatarea and to handle those products. If the person is unknown orunauthorized, the system may sound an alarm and/or generate anotheralert.

In step 358, the two (or more) images are compared. Based upon thiscomparison, it is determined whether a direction can be determined instep 376. If so, the direction of the movement is recorded in step 362.If not, then “direction unknown” is recorded in step 360. The systemgoes into waiting in step 354.

Referring to the example in FIG. 15, the system has determined thedirection (outbound, i.e. onto the truck), the date/time, the RFID ofthe pallet 22. The system may optionally also validate the load basedupon the image(s) taken of the loaded pallet 22 (using the techniquesdescribed above but with the image(s) from the camera 84). In otherwords, the image(s) taken by the camera 84 could also operate as thevalidation station 32 described above, either instead of the validationstation 32 or in supplement to the validation station 32. These imagescould be used to identify the products on the pallet 22. Alternatively,the image of the loaded pallet 22 could be compared by one of thecomputers to one or more of the images of the same loaded pallet 22 atthe validation station 32 to make sure that there have been no changes(nothing has been removed or added). This could be done with or withoutspecifically identifying every item on the pallet 22, e.g. justcomparing the two images as a whole.

With the loaded pallet 22 identified by pallet RFID, and the direction(loading or unloading determined), the system can determine that theparticular pallet 22 is being loaded onto a correct truck or anincorrect truck based upon the loading assignments previously determinedas described above. The system also determines whether the particularpallet 22 is being loaded in the correct or incorrect sequence bycomparing it to the previously-determined loading sequence describedabove. If the pallet 22 is being loaded onto the wrong truck, or out ofsequence, an alert would be generated (visually such as via display 82and/or audibly). The system can then verify that the same pallet 22 issubsequently unloaded from that truck based upon a determination thatthe pallet 22 is moved in the direction off the truck.

FIGS. 16-18 show the system operating with respect to an inbound loadedpallet 22. In FIG. 16, the breakbeam sensor 316 is triggered. In FIG.17, the rfid signal tag 94 is recorded by the RID reader 314. In FIG.18, the camera 84 takes a photo of the loaded pallet 22 and/or thedriver/operator.

In FIG. 19 the system has determined that the loaded pallet was inbound,the date/time, the pallet id, and the identification of the operator.

Additional features for post processing can be implemented after eventsare recorded. Visual indicators can affirm or deny accuracy of assetmovement. Additional audible alarms can be generated in cases whereoperator alerting is urgent or critical. Email/text alerts can be sentwith photos of threshold events (e.g. a high value asset being loaded onto incorrect truck). Shipment claim processing can also be supported,such as photographic verification items left warehouse.

In accordance with the provisions of the patent statutes andjurisprudence, exemplary configurations described above are consideredto represent preferred embodiments of the inventions. However, it shouldbe noted that the inventions can be practiced otherwise than asspecifically illustrated and described without departing from its spiritor scope. Alphanumeric identifiers on method steps are solely for easein reference in dependent claims and such identifiers by themselves donot signify a required sequence of performance, unless otherwiseexplicitly specified.

What is claimed is:
 1. A delivery portal comprising: a sensor configuredto detect a pallet as it passes through the portal; and a computerprogrammed to receive information from the sensor and to identify thepallet based upon the information, the computer further programmed tocompare the identified pallet to a database to determine if theidentified pallet should be passing through the portal.
 2. The deliveryportal of claim 1 wherein the sensor is an RFID sensor.
 3. The deliveryportal of claim 2 wherein the portal is a loading dock.
 4. The deliveryportal of claim 3 wherein the database indicates a sequence for loadinga plurality of pallets including the identified pallet onto a truck atthe loading dock.
 5. The delivery portal of claim 4 further including acamera wherein the computer is programmed to receive images from thecamera.
 6. The delivery portal of claim 5 wherein the computer isprogrammed to identify a person moving the pallet through the portal. 7.The delivery portal of claim 5 wherein the computer is programmed todetermine a direction of travel of the pallet through the portal.
 8. Thedelivery portal of claim 7 wherein the computer determines the directionof travel based upon information from the camera.
 9. The delivery portalof claim 8 wherein the computer determines the direction of travel basedupon a plurality of images from the camera.
 10. The delivery portal ofclaim 5 further including a presence sensor, wherein the computer isprogrammed to activate the RFID sensor based upon information from thepresence sensor.
 11. The delivery portal of claim 10 wherein thepresence sensor is a breakbeam sensor.
 12. The delivery portal of claim1 further including a camera, wherein the computer is programmed toidentify items on the pallet based upon an image from the camera.
 13. Adelivery portal sensor tower comprising: a housing; an RFID sensormounted to the housing; a camera mounted to the housing; and a presencesensor mounted to the housing.
 14. The delivery portal sensor tower ofclaim 13 further including a computer in communication with the RFIDsensor, the camera and the presence sensor.
 15. The delivery portalsensor tower of claim 14 wherein the computer is programmed to cause theRFID sensor to read an RFID tag and to cause the camera to generate animage based upon an indication of presence by the presence sensor. 16.The delivery portal sensor tower of claim 15 wherein the computer isprogrammed to identify a plurality of items on a pallet based upon theimage generated by the camera.
 17. A computerized method for operating aportal including: a) identifying a platform carrying a plurality ofitems stacked thereon near a truck; b) receiving the identity of theplatform in computer; c) the computer comparing the identified platformto a list indicating whether the identified platform should be loadedonto the truck; and d) the computer generating an indication whether theidentified platform should be loaded onto the truck.
 18. The method ofclaim 17 wherein the platform is a pallet.
 19. The method of claim 18wherein the list indicates a sequence of loading a plurality of palletsincluding the identified pallet and wherein step c) includes comparingthe identified pallet to the list to determine whether others of theplurality of pallets on the list should be loaded onto the truck beforethe identified pallet.
 20. The method of claim 19 wherein said step a)includes reading an RFID tag on the pallet.
 21. The method of claim 20further including the step of using a camera to image the platform and aperson moving the platform.
 22. The method of claim 17 further includingthe step of determining a direction of movement of the platform relativeto the truck.
 23. The method of claim 17 further including the step of:using a camera to generate an image of the platform and a plurality ofitems on the platform, and identifying the plurality of items on theplatform based upon the image.